INNOVATING HOME HEALTH DELIVERY TO IMPROVE VETERANS’ ACCESS TO CARE AND CARE COORDINATION

Abstract Nearly 350,000 Veterans receive home health services through the Veterans Health Administration (VA) annually. Historically, VA has purchased most home health services from contract agencies in the community. VA leaders have expressed growing concerns about Veterans’ access to home health, especially in rural areas. Coordinating care between VA and contract agencies is an ongoing challenge. To address these challenges, the VA Midwest Health Care Network (VISN 23) developed the VA Home Health (VAHH) pilot program. The program launched at the Iowa City VA and is currently operating at 4 sites in Iowa and South Dakota. Through VAHH, local VA Medical Centers hire home health staff directly (nurses, aides, physical and occupational therapists), rather than purchase these services through contract agencies. In this presentation, we describe program development, challenges, and early evaluation findings. In fiscal year (FY) 2022, 200 Veterans received VAHH. So far in FY2023, over 700 Veterans have been served. Pre/post comparisons involving Veterans receiving VAHH show modest reductions in emergency room visits (727 vs. 664 visits) and inpatient admissions (227 vs. 192 admissions). Overall hospital lengths of stay have reduced more dramatically (2132 vs. 1390 total days). VAHH staff note close communication with Veterans’ primary care teams, and Veterans describe being highly satisfied with the program. Interest in VAHH is growing nationally, particularly as access to home health in many regions is limited. Early lessons from the VAHH pilot program could inform VA decisions about home health delivery and assist other sites in improving care for Veterans.

concept to understand and recognize.This study aims to examine the effectiveness of an innovative pedagogy to reduce benevolent ageism among undergraduates majoring in healthcare with an aging focus.Two classes (36 students in the intervention group and 43 in the control group) that were both enrolled in an entry-level gerontology class were recruited.The intervention consisted of a semester-long synchronized lecture with pre-lecture quizzes, videos, examples, and in-class group projects regarding benevolent ageism, while the control group received a regular gerontology class with no additional emphasis on ageism.At the beginning and the end of the semester, all participants completed a questionnaire that was built upon the translated Ambivalent Ageism Scale (AAS) benevolent subscale.Mixed-design ANOVA was conducted to examine the effects of the pedagogy (intervention vs. control) and time (pre-test and post-test) on students' benevolent score.The score for both groups did not differ at pre-test, and the results showed a significant interaction of group by time (F (1, 77) =13.24, p < .001).The benevolent subscale score of the intervention group decreased significantly over time, while the control group only showed a slight but insignificant decrease in the score.Findings from our study suggested that this innovative pedagogy may be useful in reducing benevolent ageism among healthcare major undergraduates.However, further randomized studies are needed to confirm our findings.

REFRAMING AGING FOR HIGHER EDUCATION IN HEALTH CARE PROFESSIONS
Caitlyn Coyle, Cindy Bui, Setarreh Massihzadegan, Suha Ballout, and Jan Mutchler, University of Massachusetts Boston, Boston, Massachusetts, United States As students in health care professions will serve older patients, it is critical to proactively reframe aging in their education, confront implicit biases, and encourage age-inclusivity before they enter the workforce.We developed a training to address the limited focus on aging and later life among undergraduate curricula in health care professional disciplines (e.g., nursing, health sciences).The 1-hour, online training was piloted among current undergraduate nursing students (N=69).The content uses data and real-person interviews to introduce demographic trends in aging, define and deconstruct ageism concepts, illustrate the impact of ageism on individual health and health care, and highlight the diverse experiences of aging.The training also includes communication tools and interactive activities, such as reflection questions and case studies, to engage students in their own aging experience and to garner empathy for others' aging experiences.Pre-and post-training evaluations revealed that the training impacted students in expressing positive behavioral change towards age-inclusivity and awareness at internal, interpersonal and institutional levels.Evaluation results also showed statically significant evidence that the training increased students' knowledge about facts versus stereotypes about aging, confidence in understanding ageism and its relevance to health care, and consideration of working with older adults in their career.This pilot research project encourages other higher education institutions to incorporate training and education in their curricula that distinctively focuses on later life and reframing aging in a long-term effort towards improving health care for older people and the experiences of health care professionals who serve them.

LATE BREAKING: CLINICAL PRACTICE INNOVATIONS II
Abstract citation ID: igad104.3773

A DEPRESCRIBING CURRICULAR FRAMEWORK USING AN INTER-PROFESSIONAL APPROACH: IMPLICATIONS FOR NURSING EDUCATION
Winnie Sun 1 , Cheryl Sadowski 2 , Lalitha Raman-Wilms 3 , and Camille Gagnon 4 , 1. Ontario Tech University, Lindsay, Ontario, Canada, 2. University of Alberta, Edmonton, Alberta, Canada, 3. University of Manitoba, Winnipeg, Manitoba, Canada, 4. Institut Universitaire de Gériatrie de Montréal, Montreal, Quebec, Canada Background Deprescribing is an important component of managing polypharmacy and reducing harm from potentially inappropriate medications.Current undergraduate nursing education does not consistently incorporate components of deprescribing into curricula.It is essential to bridge the gap in promoting deprescribing competencies, teach related knowledge and skills and assess learning outcomes.The purpose of this presentation is to engage nursing educators who teach geriatrics to identify and implement a curriculum framework for deprescribing.Methodology The Canadian Medication Appropriateness and Deprescribing Network (CaDeN) Healthcare Professional Committee undertook a consensus approach to developing competencies for deprescribing, along with literature review and analysis of prescribing competencies.The authors outlined the required knowledge and skills related to the competencies, with recommended teaching and assessment strategies.Results The seven deprescribing competencies include: gathering and interpreting patients' medication history and clinical information within their context, using tools that help identify potentially inappropriate medications, weighing potential benefit and harm of continuing or deprescribing medications, using shared decision-making about deprescribing, communicating deprescribing and monitoring plans, and monitoring progress and outcomes.Integrating deprescribing competencies in nursing curricula requires an intentional and structured approach across all years of the program, focusing on interprofessional collaboration.Learning activities should be active and practical, progressing from early to advanced learner skills and include integration of deprescribing through experiential education.

PREDICTORS OF PHYSICAL FUNCTION DECLINE AMONG OLDER ADULTS RECEIVING HOME CARE NURSING: PROSPECTIVE COHORT STUDY
Taisuke Yasaka 1 , Ayumi Igarashi 1 , Chie Fukui 2 , Asa Inagaki 1 , Yuka Sumikawa 1 , Manami Takaoka 1 , Sameh Eltaybani 1 , and Noriko Yamamoto-Mitani 1 , 1.The University of Tokyo, Tokyo, Tokyo, Japan, 2. YuPia Inc., Nagoya, Aichi, Japan Older adults receiving home care nursing have certain health conditions and face continuous risk of declining physical functions.Identifying those at risk of functional decline based on these factors is essential for developing appropriate strategies for maintaining their conditions.This prospective cohort study in home care nursing agencies across Japan examined ADL decline incidence and its factors among clients aged ≥75.Data were collected from nurses in charge of clients using online questionnaires.Functional status was evaluated using the ADL Hierarchy (ADLH) score, ranging from 0 (independent) to 6 (dependent).A binary logistic regression examined the relationships between clients' characteristics at baseline and ADLH score deterioration after 6 months, controlling for ADLH score at the baseline.Of the 715 clients analyzed, 31% had deteriorated ADLH scores between baseline and 6 months later.Older age (Adjusted odds ratio 1.04 [95% confidence interval 1.01-1.07]),having neurology diseases (2.23 [1.06-4.69]),use of home visiting physicians (1.54 [1.04-2.27]),falls with/without trauma in the previous 6 months (2.77 [1.41-5.44], 1.90 [1.11-3.25]),higher ADLH score at baseline (1.35 [1.18-1.56]),higher severity of dementia (1.23 [1.02-1.47]),occurring dyspnea in the previous 30 days (1.69 [1.05-2.71]),and having BMI lower than 18.5 (between 25 and 30 as a reference) (2.58 [1.12-5.94])were associated with ADLH score deteriorations.The results suggest that inhibiting functional decline among older adults receiving home care nursing might be possible by optimizing the management of modifiable predictors such as frequent falls, dyspnea, and weight loss.

THE PREDICTIVE EFFECT OF DIFFERENT MACHINE LEARNING ALGORITHMS FOR PRESSURE INJURIES:
A NETWORK META-ANALYSES Chaoran Qu, Xiufen Yang, Weisi Peng, Xiujuan Wang, and Weixiang Luo, 1. Shenzhen People 's Hospital, Shenzhen, Guangdong, China (People's Republic) Objective: This review aims to systematically synthesize existing evidence to determine the effectiveness of applying machine learning algorithms for pressure injury management, to further evaluate and compare pressure injury prediction models constructed by numerous machine learning algorithms, and to derive evidence for the best algorithms for predicting and managing pressure injuries.Methods: A systematic electronic search was conducted in the EBSCO, Embase, PubMed, and Web of Science databases.We included all retrospective diagnostic accuracy trials and prospective diagnostic accuracy trials constructing a predictive model by machine learning for pressure injuries up to December 2021.The network meta-analysis was conducted using statistical software R and STATA.The certainty of the evidence was rated using the QUADAS-2 tool.Result: Twenty-five clinical diagnostic trials with a total of 237397 participants were identified in this review.The results of our study revealed that pressure injury machine learning models can effectively predict these injuries.Combining the algorithms separately yields the main results: decision trees (sensitivity: 0.66, specificity: 0.90, AUC: 0.88), logistic regression (sensitivity: 0.71, specificity: 0.83, AUC: 0.84), neural networks (sensitivity: 0.73, specificity: 0.78, AUC: 0.82), random forests (sensitivity: 0.72, specificity: 0.96, AUC: 0.95), support vector machines (sensitivity: 0.81, specificity: 0.81, AUC: 0.88).According to the analysis of ROC and AUC values, random forest is the best algorithm for the prediction model of pressure injury.Conclusions: This review revealed that machine learning algorithms are generally effective in predicting pressure injuries, the random forest algorithm is the best algorithm for pressure injury prediction.
, Grand Island, Nebraska,  United States, 2. VA Iowa City VA Health Care System,  Iowa City, Iowa, United States, 3. Sioux Falls VA Health  Care System, Sioux Falls, South Dakota, United States, 4.  VA Black Hills Health Care System, Fort Meade, South  Dakota, United States, 5. Iowa City VA Health Care  System, Iowa City, Iowa, United States, 6. Central Iowa VA  Health Care System, Des Moines, Iowa, United StatesNearly 350,000 Veterans receive home health services through the Veterans Health Administration (VA) annually.Historically, VA has purchased most home health services from contract agencies in the community.VA leaders have expressed growing concerns about Veterans' access to home health, especially in rural areas.Coordinating care between VA and contract agencies is an ongoing challenge.To address these challenges, the VA Midwest Health Care Network (VISN 23) developed the VA Home Health (VAHH) pilot program.The program launched at the Iowa City VA and is currently operating at 4 sites in Iowa and South Dakota.Through VAHH, local VA Medical Centers hire home health staff directly (nurses, aides, physical and occupational therapists), rather than purchase these services through contract agencies.In this presentation, we describe program development, challenges, and early evaluation findings.In fiscal year (FY) 2022, 200 Veterans received VAHH.So far in FY2023, over 700 Veterans have been served.Pre/post comparisons involving Veterans receiving VAHH show modest reductions in emergency room visits (727 vs. 664 visits) and inpatient admissions(227 vs. 192 admissions).Overall hospital lengths of stay have reduced more dramatically (2132 vs. 1390 total days).VAHH staff note close communication with Veterans' primary care teams, and Veterans describe being highly satisfied with the program.Interest in VAHH is growing nationally, particularly as access to home health in many regions is limited.Early lessons from the VAHH pilot program could inform VA decisions about home health delivery and assist other sites in improving care for Veterans.